A general framework of noise suppression in material decomposition for dual‐energy CT

Petrongolo, Michael; Dong, Xue; Zhu, Lei

doi:10.1118/1.4926780

Cited by 25 publications

(26 citation statements)

References 43 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, we did not apply any noise‐reduction algorithms when generating the material maps, since image noise was sufficiently low in the original images. However, it has been shown that for DECT, the linear material decomposition leads to large and anticorrelated noise in the material maps . Similarly, base material images from multienergy imaging e.g., spectral photon‐counting scans, suffer from increased image noise.…”

Section: Discussionmentioning

confidence: 99%

“…However, it has been shown that for DECT, the linear material decomposition leads to large and anticorrelated noise in the material maps. 33,34 Similarly, base material images from multienergy imaging e.g., spectral photon-counting scans, suffer from increased image noise. Although algorithms have been developed for dual-energy CT to cope with this challenge, the application of these methods to multienergy CT is beyond the scope of this article.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Photon‐counting CT for simultaneous imaging of multiple contrast agents in the abdomen: An in vivo study

et al. 2017

View full text Add to dashboard Cite

Purpose To demonstrate the feasibility of spectral imaging using photon-counting detector (PCD) x-ray computed tomography (CT) for simultaneous material decomposition of 3 contrast agents in vivo in a large animal model. Methods This Institutional Animal Care and Use Committee-approved study used a canine model. Bismuth subsalicylate was administered orally 24–72 hours before imaging. PCD CT was performed during intravenous administration of 40–60 ml gadoterate meglumine; 3.5 minutes later, iopamidol 370 was injected intravenously. Renal PCD CT images were acquired every 2 seconds for 5–6 minutes to capture the wash-in and wash-out kinetics of the contrast agents. Least mean squares linear material decomposition was used to calculate the concentrations of contrast agents in the aorta, renal cortex, renal medulla and renal pelvis. Results Using reference vials with known concentrations of materials, we computed molar concentrations of the various contrast agents during each phase of CT scanning. Material concentration maps allowed simultaneous quantification of both arterial and delayed renal enhancement in a single CT acquisition. The accuracy of the material decomposition algorithm in a test phantom was −0.4±2.2 mM, 0.3±2.2 mM for iodine and gadolinium solutions, respectively. Peak contrast concentration of gadolinium and iodine in the aorta, renal cortex, and renal medulla were observed 16, 24, and 60 seconds after the start each injection, respectively. Conclusion Photon-counting spectral CT allowed simultaneous material decomposition of multiple contrast agents in vivo. Besides defining contrast agent concentrations, tissue enhancement at multiple phases was observed in a single CT acquisition, potentially obviating the need for multi-phase CT scans and thus reducing radiation dose.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Photon‐counting CT for simultaneous imaging of multiple contrast agents in the abdomen: An in vivo study

et al. 2017

View full text Add to dashboard Cite

show abstract

“…(Yu et al , 2012) It should be noted, however, that our research presented in this paper is focused on the design of a new reconstruction algorithm for DECT independently of the decomposition process, and the use of SPIR does not require an image-domain decomposition. For example, we can first perform a non-linear decomposition on the dual-energy projection data using the same method as shown in our recent publication,(Petrongolo et al , 2015) and then carry out SPIR on the decomposed projections.…”

Section: Discussionmentioning

confidence: 99%

Dual energy CT with one full scan and a second sparse-view scan using structure preserving iterative reconstruction (SPIR)

Wang

Zhu

2016

Phys. Med. Biol.

Self Cite

View full text Add to dashboard Cite

Conventional dual-energy CT (DECT) reconstruction requires two full-size projection datasets with two different energy spectra. In this study, we propose an iterative algorithm to enable a new data acquisition scheme which requires one full scan and a second sparse-view scan for potential reduction in imaging dose and engineering cost of DECT. A bilateral filter is calculated as a similarity matrix from the first full-scan CT image to quantify the similarity between any two pixels, which is assumed unchanged on a second CT image since DECT scans are performed on the same object. The second CT image from reduced projections is reconstructed by an iterative algorithm which updates the image by minimizing the total variation of the difference between the image and its filtered image by the similarity matrix under data fidelity constraint. As the redundant structural information of the two CT images is contained in the similarity matrix for CT reconstruction, we refer to the algorithm as structure preserving iterative reconstruction (SPIR). The proposed method is evaluated on both digital and physical phantoms, and is compared with the filtered-backprojection (FBP) method, the conventional total-variation-regularization-based algorithm (TVR) and Prior-Image-Constrained-Compressed-Sensing (PICCS). SPIR with a second 10-view scan reduces the image noise STD by a factor of one order of magnitude with same spatial resolution as full-view FBP image. SPIR substantially improves over TVR on the reconstruction accuracy of a 10-view scan by decreasing the reconstruction error from 6.18% to 1.33%, and outperforms TVR at 50 and 20-view scans on spatial resolution with a higher frequency at the modulation transfer function value of 10% by an average factor of 4. Compared with the 20-view scan PICCS result, the SPIR image has 7 times lower noise STD with similar spatial resolution. The electron density map obtained from the SPIR-based DECT images with a second 10-view scan has an average error of less than 1%.

show abstract

“…Furthermore, although we focus our paper on linear image-domain decomposition of DECT, the proposed method is readily translatable to nonlinear projection-domain decomposition, using the same technique as shown in our recent paper. 18 The PWLS-SBR algorithm has two indications beyond the scope of DECT imaging. First, our results reveal that the similarity-based regularization is superior in preservation of image NPS compared with gradient-based regularization, although the latter is widely used for retaining edges during noise suppression.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…The material decomposition can be performed in either the projection domain [13][14][15][16][17][18] or the image-domain. 11,19,20 Decomposition in the projection domain has the advantage of being able to correct for beam-hardening artifacts.…”

Section: Introductionmentioning

confidence: 99%

Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

et al. 2016

Self Cite

View full text Add to dashboard Cite

Purpose: Dual-energy CT (DECT) expands applications of CT imaging in its capability to decompose CT images into material images. However, decomposition via direct matrix inversion leads to large noise amplification and limits quantitative use of DECT. Their group has previously developed a noise suppression algorithm via penalized weighted least-square optimization with edge-preservation regularization (PWLS-EPR). In this paper, the authors improve method performance using the same framework of penalized weighted least-square optimization but with similarity-based regularization (PWLS-SBR), which substantially enhances the quality of decomposed images by retaining a more uniform noise power spectrum (NPS). Methods: The design of PWLS-SBR is based on the fact that averaging pixels of similar materials gives a low-noise image. For each pixel, the authors calculate the similarity to other pixels in its neighborhood by comparing CT values. Using an empirical Gaussian model, the authors assign high/low similarity value to one neighboring pixel if its CT value is close/far to the CT value of the pixel of interest. These similarity values are organized in matrix form, such that multiplication of the similarity matrix to the image vector reduces image noise. The similarity matrices are calculated on both high-and low-energy CT images and averaged. In PWLS-SBR, the authors include a regularization term to minimize the L-2 norm of the difference between the images without and with noise suppression via similarity matrix multiplication. By using all pixel information of the initial CT images rather than just those lying on or near edges, PWLS-SBR is superior to the previously developed PWLS-EPR, as supported by comparison studies on phantoms and a head-and-neck patient. Results: On the line-pair slice of the Catphan C 600 phantom, PWLS-SBR outperforms PWLS-EPR and retains spatial resolution of 8 lp/cm, comparable to the original CT images, even at 90% reduction in noise standard deviation (STD). Similar performance on spatial resolution is observed on an anthropomorphic head phantom. In addition, results of PWLS-SBR show substantially improved image quality due to preservation of image NPS. On the Catphan C 600 phantom, NPS using PWLS-SBR has a correlation of 93% with that via direct matrix inversion, while the correlation drops to −52% for PWLS-EPR. Electron density measurement studies indicate high accuracy of PWLS-SBR. On seven different materials, the measured electron densities calculated from the decomposed material images using PWLS-SBR have a root-mean-square error (RMSE) of 1.20%, while the results of PWLS-EPR have a RMSE of 2.21%. In the study on a head-and-neck patient, PWLS-SBR is shown to reduce noise STD by a factor of 3 on material images with image qualities comparable to CT images, whereas fine structures are lost in the PWLS-EPR result. Additionally, PWLS-SBR better preserves low contrast on the tissue image. Conclusions: The authors propose improvements to the regularization term of an optimization frame...

show abstract

A general framework of noise suppression in material decomposition for dual‐energy CT

Cited by 25 publications

References 43 publications

Photon‐counting CT for simultaneous imaging of multiple contrast agents in the abdomen: An in vivo study

Photon‐counting CT for simultaneous imaging of multiple contrast agents in the abdomen: An in vivo study

Dual energy CT with one full scan and a second sparse-view scan using structure preserving iterative reconstruction (SPIR)

Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

Contact Info

Product

Resources

About